title: Self-organization of associative memory and pattern classification: recurrent signal processing on topological feature maps.
abstract: We extend the neural concepts of topological feature maps towards self-organization of auto-associative memory and hierarchical pattern classification. As is well-known, topological maps for statistical data sets store information on the associated probability densities. To extract that information we introduce a recurrent dynamics of signal processing. We show that the dynamics converts a topological map into an auto-associative memory for real-valued feature vectors which is capable to perform a cluster analysis. The neural network scheme thus developed represents a generalization of non-linear matrix-type associative memories. The results naturally lead to the concept of a feature atlas and an associated scheme of self-organized, hierarchical pattern classification.
authors.affiliation: Physik-Department, Technische Universität München, Garching, Federal Republic of Germany.
authors.forename: P
authors.initials: P
authors.lastname: Tavan
authors.forename: H
authors.initials: H
authors.lastname: Grubmüller
authors.forename: H
authors.initials: H
authors.lastname: Kühnel
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology; D003491:Cybernetics; D006801:Humans; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D015398:Signal Transduction / Q000502:physiology